1. Field of the Invention
The claimed invention relates to methods and systems for evaluating an electrophysiological signal.
2. Description of Related Art
Frequently, important physiological information can be captured as electrophysiological signals. Some examples include, but are not limited to, electrocardiogram (ECG) signals, voiced speech signals, electrooculogram (EOG) and electromyogram (EMG) signals, vestibuloocular response signals, blood pressure gamma synchrony signals (based on electroencephalogram (EEG) measurements), a respiratory function signal, a pulse oximetry signal (measuring the oxygenation of a patient's blood), a perfusion data signal (measuring changes in tissue images following introduction of a contrast agent to the blood), and quasi-periodic biological signals.
The article by Korenberg and Paarmann (“Applications of Fast Orthogonal Search: Time-Series Analysis and Resolution of Signals in Noise”, Ann. Biomed. Eng., Vol. 17, pp. 219-231, 1989), which is hereby incorporated by reference in its entirety, specifically relates the application of Fast Orthogonal Search (FOS) to several of the above electrophysiological signals, including ECG, EEG, EOG, and EMG signals, and shows that FOS can recover signals heavily contaminated with noise. In particular FOS is applied to the signal, a plurality of nonlinear terms (e.g. sinusoidal functions) are generated corresponding to the signal, a noise component is separated from the plurality of nonlinear terms corresponding to the signal, and a reconstructed signal is formed whereby the noise component is removed by using a subset of the plurality of nonlinear terms corresponding to the signal (see Example 3: Noisy Data Case, and FIGS. 1-3, on pages 228-230 of this article). The article discloses that FOS can be used to find accurate and parsimonious sinusoidal series models for such electrophysiological signals. FOS finds the terms in the series by searching through a set of candidate terms. The sinusoidal series developed in the article, sums of cosine and sine functions, are examples of summation series of complex exponentials. Here a cosine can be the real part, and a sine can be the imaginary part, of a complex exponential. It should be noted that the sinusoidal terms in such series are fractionally differentiable and integrable analytically, where the order of the fractional derivative or integral can be any real or complex number. A derivative of negative order −a, where a >0, corresponds to an integral of positive order a. A derivative of zero order of a function is just the function itself.
In the article by Adeney and Korenberg (IEE Proc.-Vis. Image Signal Process. Vol. 141, No. 1, pp. 13-18, 1994), which is hereby incorporated by reference in its entirety, FOS and Iterative FOS (IFOS) are used to find a sum of complex sinusoids, which is also a summation series of complex exponentials. Both FOS and IFOS are shown to be very powerful methods for dealing with noise contamination, for signal-to-noise ratios (SNR) as low as −10 dB.
Chon (IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 48, NO. 6, pp 622-629, June 2001) also shows that FOS can be used to detect periodic frequency components in both cardiovascular and renal signals, separating these components “from the unwanted stochastic component (noise source)”. FOS was shown to be effective even when the SNR was as low as −20 dB, and was “undeterred by the adverse effects of colored, white and 1/f noise present in the data”.
U.S. Pat. No. 6,325,761 (Dec. 4, 2001) to Jay discloses a device and method for measuring pulsus paradoxus (“a quantifiable, exaggerated decrease in arterial blood pressure during inspiration”), using as input data a waveform indicative of patient pulsatile cardiovascular behavior from an optical plethysmograph, a pulse oximeter, or a blood pressure monitor. The invention can assess the status of a patient in acute respiratory distress to determine severity of the condition, and one embodiment uses FOS to fit a sinusoidal series to the data for measurement of pulsus paradoxus and display. Thus the invention describes a method of evaluating an electrophysiological signal, including receiving an electrophysiological signal, obtaining a model-derived reconstruction using a summation series of complex exponentials (here a sinusoidal series) over at least one cycle of the electrophysiological signal to identify a pathological condition (pulsus paradoxus), and display on a user interface data indicative of pulsus paradoxus, and predict the risk for adverse clinical outcomes, such as impending severe respiratory distress.
Before U.S. Provisional Application Ser. No. 61/462,640 was filed Feb. 4, 2011, a Gupta et al presentation (COMPLEX SUB-HARMONIC STRUCTURES AS A PREDICTOR FOR ICD THERAPY, Abstract at the Canadian Cardiovascular Congress, Montreal, Canada, Oct. 23-27, 2010) considered the use of FOS on high-resolution ECG data, and “Risk stratification for sudden cardiac death (SCD)”. They hypothesized that “a contemporary algorithm” [they used FOS] “which is capable of detecting aperiodic complex sub-harmonic frequencies (CSF) may detect differences in the ECG spectra of patients demonstrating SCD potential when compared with controls” and provided supporting experimental results.
The human heart 20, schematically illustrated in FIG. 1, has four contractile chambers which work together to pump blood throughout the body. The upper chambers are called atria, and the lower chambers are called ventricles. The right atrium 22 receives blood 24 that has finished a tour around the body and is depleted of oxygen. This blood 24 returns through the superior vena cava 26 and inferior vena cava 28. The right atrium 22 pumps this blood through the tricuspid valve 30 into the right ventricle 32, which pumps the oxygen-depleted blood 24 through the pulmonary valve 34 into the right and left lungs 36, 38. The lungs oxygenate the blood, and eliminate the carbon dioxide that has accumulated in the blood due to the body's many metabolic functions. The oxygenated blood 40 returns from the right and left lungs, 36, 38 and enters the heart's left atrium 42, which pumps the oxygenated blood 40 through the bicuspid valve 44 into the left ventricle 46. The left ventricle 46 then pumps the blood 40 through the aortic valve 48 into the aorta 50 and back into the blood vessels of the body. The left ventricle 46 has to exert enough pressure to keep the blood moving throughout all the blood vessels of the body. The contractions of the heart's chambers are controlled by electrochemical mechanisms which generate an electrophysiological signal that can be measured. In the case of the heart, the electrophysiological signal can be captured as an electrocardiogram (ECG).
The human brain 52 is an even more complex part of the body, composed of bundles of electrically active neurons, and organized into functional regions such as the cerebrum 54, the brain stem 56, and the cerebellum 58 as schematically illustrated in FIG. 2. The cerebrum 54 is responsible for conscious behavior and has areas for motor, sensory, and association functions. The brain stem 56, which contains the medulla oblongata plays an important role as an autonomic reflex center involved in maintaining the body's homeostasis. In particular, this portion of the brain regulates heart rate, respiratory rhythm, and blood pressure. The cerebellum 58 processes neural impulses received from the cerebral motor cortex, various brain stem nuclei, and sensory receptors in order to appropriately control skeletal muscle contractions to enable smooth, coordinated movements. The neural activity of the brain also involves electrochemical mechanisms which generate an electrophysiological signal that can be measured. In the case of the brain, the electrophysiological signal can be captured as a electroencephalogram (EEG).
With the ongoing proliferation of data acquisition devices, more and more physiological aspects are able to be captured as electrophysiological signals. Some examples include, but are not limited to, gamma synchrony signals (based on EEG measurements), a respiratory function signal, a pulse oximetry signal (measuring the oxygenation of a patient's blood), a perfusion data signal (measuring changes in tissue images following introduction of a contrast agent to the blood), and quasi-periodic biological signals.
Devices which capture electrophysiological signals may be valuable tools for physicians to study the health conditions of a patient. After the recording of the electrophysiological signal, it is up to the physician or healthcare provider to perform the signal analysis. For example, in the case of ECG signal analysis, there are certain integrated automatic analysis processes and systems which automatically determine different types of heart beats, rhythms, etc. The traditional output from the existing ECG software is basic data that often needs to be supplemented by a Cardiac MRI (Magnetic Resonance Imaging), CT (Computed tomography) or a more invasive test. However, there are a number of limitations associated with all such systems described above, they are complex, their outputs are difficult to analyze, and such techniques are expensive to use.
In addition to the above systems, there are various time domain and frequency domain signal processing techniques which are being used for the analysis of electrophysiological signals to obtain more detailed information. Unfortunately, the time domain techniques are incapable of quantifying certain fluctuation characteristics of a number of pathologies related to the electrophysiological signal. For example, with regard to the heart, traditional methods for performing frequency-domain analysis of surface ECG signals, such as the Fourier transform, are limited since they do not address the random nature of biological and electromagnetic noise or the variation between patients.
For example, in case of arrhythmia, the heart generates very complex ECG waveforms that have a large variation in morphologies. Dominant frequency analysis on these ECGs can be problematic since non-linear dynamic systems can appear to generate random noise. Discrete fast Fourier transforms and wavelet analysis have been shown experimentally to be incapable of detecting deterministic chaos in the presence of strong periodicity which tends to obscure the underlying non-linear structures. Thus, the detection of complex sub-harmonic frequencies which are thought to exist in all arrhythmia requires dynamic non-linear analyses. Complex subharmonic frequencies are similarly thought to exist in other types of electrophysiological signals and may be indicative of other pathological events which are not otherwise detectable from the electrophysiological signal using prior art methods.
Therefore, there is a need for a reliable and efficient system and method for evaluating an electrophysiological signal to predict pathological events with high accuracy.